The overall goal of this proposal is to identify potential prognostic markers of colorectal adenocarcinoma (CRC). Despite numerous published studies on prognostic markers of CRC, no new marker has achieved accepted clinical utility due to controversial results. Recent studies to resolve some of the controversies, from the laboratories of others and ours, have suggested that prognostic value of several molecular and phenotypic features of CRCs vary with anatomic location of the tumor and/or with patient race/ethnicity. Therefore, we propose to identify the molecular markers of sporadic CRCs that are associated with aggressiveness of the tumor hence prognosis of African-American and Caucasian patients. Since the comprehensive evaluation of vast amounts of data requires powerful statistical techniques, we propose to use knowledge discovery tools to identify potentially useful information to predict patient prognosis. In Specific Aim 1, stage-and site-matched 750 CRC patient populations each, of African-Americans and Caucasians will be evaluated for phenotypic expression of several key molecular markers which are likely to be acceptable as prognosticators. The markers to be analyzed immunohistochemically (IHC) are: DCC (deleted in colon cancer), p53, p21waf-1, p27Kip-1, Mdm2, cyclin E, Ki67, Bcl-2, Bax, MUC1, cyclooxygenase 2, and thymidylate synthase. The extent of apoptosis will be determined utilizing TUNEL. In Specific Aim 2, DNA extracted from archival tissues will be analyzed for microsatellite instability (MSI) at 4 CA-dinucleotide MS loci (Mfd27, Mfd41, Mfd47, and Mfd57) and 2 poly-A repeats (BAT25, BAT26). In addition, the tissue array sections will be evaluated for the expression of hMLH1 and hMSH2 to determine DNA mismatch repair deficient tumors. The phenotypic variations and the level of MSI will be correlated with the prognosis of African-Americans and Caucasians based on the anatomic location of the tumor using statistical methods. In Specific Aim 3, we will use data mining tools to identify the complex relationships of multiple factors in data sets generated in Specific Aims 1 and 2. Eventually, separate prognostic models will be developed for African-American and Caucasian patients with CRC. The findings of these studies will aid the clinical oncologist in identifying aggressive forms of CRCs using MSI and phenotypic patterns, and help in selecting potential candidates for therapeutic interventions. Additionally, these strategies can be used to identify key information or decision making knowledge discoveries in vast molecular epidemiological databases that are already generated.